assignment0/numpy_warmup.ipynb

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   "source": [
    "# Numpy热身\n",
    "numpy是常用的科学计算库，下面是对numpy的一些基本操作的练习。\n",
    "\n",
    "相关文档:\n",
    "- [python3文档](https://docs.python.org/3/)\n",
    "- [numpy文档](https://numpy.org/doc/)\n",
    "- [matplotlib教程](https://matplotlib.org/stable/tutorials/index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "1.(导入numpy库) 导入numpy库并命名为np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "2.(一维数组) 建立一个一维数组 a 初始化为[4,5,6]   \n",
    "\t1)打印a    \n",
    "\t2)输出a的类型（type）    \n",
    "   3)输出a的各维度的大小（shape）      \n",
    "   4)输出a的第一个元素（值为4）    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4 5 6]\n",
      "<class 'numpy.ndarray'>\n",
      "(3,)\n",
      "4\n"
     ]
    }
   ],
   "source": [
    "a = np.array([4,5,6])\r\n",
    "\r\n",
    "print (a)\r\n",
    "print (type(a))\r\n",
    "print (a.shape)\r\n",
    "print (a[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "3.(二维数组) 建立一个二维数组b, 初始化为[[4, 5, 6],[1, 2, 3]]\t    \n",
    "\t1)输出各维度的大小（shape）\t       \n",
    "   2)输出b(0,0),b(0,1),b(1,1)这三个元素（对应值分别为4,5,2）       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2, 3)\n",
      "4 5 2\n"
     ]
    }
   ],
   "source": [
    "b = np.array([[4, 5, 6],[1, 2, 3]])\r\n",
    "\r\n",
    "print (b.shape)\r\n",
    "print (b[0][0],b[0,1],b[1,1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "4.(创建特殊矩阵) 创建并分别打印下列矩阵：    \n",
    "    1)全0矩阵a，大小为3x3，类型为整型    \n",
    "    2)全1矩阵b，大小为4x5    \n",
    "    3)单位矩阵c，大小为4x4\t    \n",
    "    4)随机数矩阵d，大小为3x2，随机种子设置为233"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 0 0]\n",
      " [0 0 0]\n",
      " [0 0 0]]\n",
      "[[1. 1. 1. 1. 1.]\n",
      " [1. 1. 1. 1. 1.]\n",
      " [1. 1. 1. 1. 1.]\n",
      " [1. 1. 1. 1. 1.]]\n",
      "[[1. 0. 0. 0.]\n",
      " [0. 1. 0. 0.]\n",
      " [0. 0. 1. 0.]\n",
      " [0. 0. 0. 1.]]\n",
      "[[0.14946913 0.8922156 ]\n",
      " [0.58316564 0.89974018]\n",
      " [0.34890549 0.14842378]]\n"
     ]
    }
   ],
   "source": [
    "a = np.zeros((3,3),dtype=int)\r\n",
    "print (a)\r\n",
    "\r\n",
    "b = np.ones((4,5))\r\n",
    "print (b)\r\n",
    "\r\n",
    "c = np.eye(4)\r\n",
    "print (c)\r\n",
    "\r\n",
    "np.random.seed(233)\r\n",
    "d = np.random.random((3,2))\r\n",
    "print (d)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "5.(数组索引与切片) 建立一个数组a,值为[[1, 2, 3, 4],[5, 6, 7, 8],[9, 10, 11, 12],[13, 14, 15, 16]]     \n",
    "\t1)打印a    \n",
    "   2)得到a中心2x2大小的矩阵[[6,7],[10,11]]，令其为b，打印b      \n",
    "   3)修改b的(0,0)位置的值为17，打印b，打印a    \n",
    "   4)打印a最后两行组成的矩阵[[9, 10, 11, 12],[13, 14, 15, 16]]（使用a[num1:num2,:]等操作）     \n",
    "   5)打印a最后一行组成的数组[13, 14, 15, 16]（使用a[-1]或者a[-1,:]等操作）   \n",
    "   6)打印a的(0,0),(1,1),(2,0)这几个位置的元素[1,17,9]（使用a[[0,1,2],[0,1,0]]等操作）    \n",
    "   7)打印a的(0,3),(1,2),(2,1),(3,2)这几个位置的元素[4,7,10,15]（使用np.arange()等操作）    \n",
    "   8)打印a的(0,3),(1,2),(2,1),(3,2)这几个位置的元素都加10后的矩阵（使用+=10等操作）     \n",
    "   9)打印8)得到的a中所有大于10的元素组成的数组（使用 bool_idx = (a>10)等操作）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1  2  3  4]\n",
      " [ 5  6  7  8]\n",
      " [ 9 10 11 12]\n",
      " [13 14 15 16]]\n",
      "[[ 6  7]\n",
      " [10 11]]\n",
      "[[17  7]\n",
      " [10 11]]\n",
      "[[ 1  2  3  4]\n",
      " [ 5 17  7  8]\n",
      " [ 9 10 11 12]\n",
      " [13 14 15 16]]\n",
      "[[ 9 10 11 12]\n",
      " [13 14 15 16]]\n",
      "[13 14 15 16]\n",
      "[13 14 15 16]\n",
      "[ 1 17  9]\n",
      "[ 4  7 10 15]\n",
      "[[ 1  2  3 14]\n",
      " [ 5 17 17  8]\n",
      " [ 9 20 11 12]\n",
      " [13 14 25 16]]\n",
      "[14 17 17 20 11 12 13 14 25 16]\n"
     ]
    }
   ],
   "source": [
    "a = np.array([[1, 2, 3, 4],[5, 6, 7, 8],[9, 10, 11, 12],[13, 14, 15, 16]])\r\n",
    "b = a[1:3,1:3]\r\n",
    "print (a)\r\n",
    "print (b)\r\n",
    "\r\n",
    "b[0,0] = 17\r\n",
    "print (b)\r\n",
    "print (a)\r\n",
    "\r\n",
    "print (a[-2:,:])\r\n",
    "print (a[-1])\r\n",
    "print (a[-1,:])\r\n",
    "print (a[[0,1,2],[0,1,0]])\r\n",
    "print (a[np.arange(4),[3,2,1,2]])\r\n",
    "\r\n",
    "a[np.arange(4),[3,2,1,2]] += 10\r\n",
    "print (a)\r\n",
    "\r\n",
    "bool_idx = (a>10)\r\n",
    "print (a[bool_idx])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "6.(数据类型与数学运算) 完成下列操作并打印相应结果    \n",
    "\t1)执行x = np.array([1, 2])，y = np.array([1.0, 2.0])，打印x,y及其内部元素的数据类型    \n",
    "   2)执行x = np.array([[1, 2],[3, 4]], dtype=np.float64)，y = np.array([[5, 6],[7, 8]], dtype=np.float64)，输出x+y, np.add(x,y), x-y, np.subtract(x,y)   \n",
    "   3)对2)中x和y执行x\\*y, np.multiply(x,y), np.matmul(x,y), np.dot(x,y)    \n",
    "   4)对2)中x和y执行x.add(y)，x.dot(y)操作(其中一个执行失败)\n",
    "   5)对2)中x和y执行对应元素相除操作x/y, np.divide(x,y)    \n",
    "   6)对2)中x执行平方、开方操作    \n",
    "   7)对2)中x执行所有元素求和, 按照行求平均, 按照列求最大值操作;然后尝试使用keepdims参数    \n",
    "   8)对2)中x转置    \n",
    "   9)对2)中x所有元素求e的指数    \n",
    "   10)对2)中x所有元素求值最大元素的下标, 再按照行和列求最大元素的下标\n",
    "   11)执行x = np.array([[1,2,3], [4,5,6], [7,8,9], [10, 11, 12]]), v = np.array([1, 0, 1],然后执行x+y操作"
   ]
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  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2] [1. 2.]\n",
      "int64 float64\n",
      "[[ 6.  8.]\n",
      " [10. 12.]]\n",
      "[[ 6.  8.]\n",
      " [10. 12.]]\n",
      "[[-4. -4.]\n",
      " [-4. -4.]]\n",
      "[[-4. -4.]\n",
      " [-4. -4.]]\n",
      "[[ 5. 12.]\n",
      " [21. 32.]]\n",
      "[[ 5. 12.]\n",
      " [21. 32.]]\n",
      "[[19. 22.]\n",
      " [43. 50.]]\n",
      "[[19. 22.]\n",
      " [43. 50.]]\n",
      "[[19. 22.]\n",
      " [43. 50.]]\n",
      "[[0.2        0.33333333]\n",
      " [0.42857143 0.5       ]]\n",
      "[[0.2        0.33333333]\n",
      " [0.42857143 0.5       ]]\n",
      "[[ 1.  4.]\n",
      " [ 9. 16.]]\n",
      "[[1.         1.41421356]\n",
      " [1.73205081 2.        ]]\n",
      "10.0\n",
      "[[1.5]\n",
      " [3.5]]\n",
      "[[1. 3.]\n",
      " [2. 4.]]\n",
      "3\n",
      "[1 1]\n",
      "[[ 1  2  3]\n",
      " [ 4  5  6]\n",
      " [ 7  8  9]\n",
      " [10 11 12]]\n",
      "[[ 2  2  4]\n",
      " [ 5  5  7]\n",
      " [ 8  8 10]\n",
      " [11 11 13]]\n"
     ]
    }
   ],
   "source": [
    "x = np.array([1, 2])\r\n",
    "y = np.array([1.0, 2.0])\r\n",
    "print (x,y)\r\n",
    "print (x.dtype,y.dtype)\r\n",
    "\r\n",
    "x = np.array([[1, 2],[3, 4]], dtype=np.float64)\r\n",
    "y = np.array([[5, 6],[7, 8]], dtype=np.float64)\r\n",
    "print (x+y)\r\n",
    "print (np.add(x,y))\r\n",
    "print (x-y)\r\n",
    "print (np.subtract(x,y))\r\n",
    "#print (x.add(y))\r\n",
    "print (x*y)\r\n",
    "print (np.multiply(x,y))\r\n",
    "print (np.matmul(x,y))\r\n",
    "print (np.dot(x,y))\r\n",
    "print (x.dot(y))\r\n",
    "print (x/y)\r\n",
    "print (np.divide(x,y))\r\n",
    "print (x**2)\r\n",
    "print (np.sqrt(x))\r\n",
    "print (x.sum())\r\n",
    "print (x.mean(axis=1,keepdims=True))\r\n",
    "print (x.T)\r\n",
    "print (np.argmax(x))\r\n",
    "print (np.argmax(x, axis=0))\r\n",
    "\r\n",
    "x = np.array([[1,2,3], [4,5,6], [7,8,9], [10, 11, 12]])\r\n",
    "v = np.array([1, 0, 1])\r\n",
    "print (x)\r\n",
    "print (x+v)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "7.(更改数组形状) 执行x = np.array([[0,1],[2,3]]),并执行下列操作    \n",
    "\t1)分别执行x.resize((1,4)), x.resize((1,2)), x.resize((2,4)), x.resize((2,3)),并打印x    \n",
    "   2)对原始x分别执行x.reshape((1,4)),x.reshape((-1,4)), x.reshape((2,4)),观察reshape操作和resize操作的区别     \n",
    "   3)尝试在上述操作中使用x.view(),观察view()操作的作用"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 1 2 3]]\n"
     ]
    }
   ],
   "source": [
    "x = np.array([[0,1],[2,3]])\r\n",
    "print (x.reshape((1,4)))\r\n",
    "# print (a.resize((1,2)))\r\n",
    "# print (a.resize((2,4)))\r\n",
    "# print (a.resize((2,3)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "8.(使用matplotlib.pyplot库作图) 导入matplotlib.pyplot库,尝试绘制下列图形     \n",
    "\t1)y = x\\*x,其中x = np.arange(0,100,0.1)     \n",
    "   2)y = sin(x), y = cos(x),其中x = x = np.arange(0, 3 * np.pi, 0.1)    "
   ]
  },
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   "cell_type": "code",
   "execution_count": 8,
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   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 使图像在当前窗口显示\r\n",
    "%matplotlib inline \r\n",
    "import matplotlib.pyplot as plt \r\n",
    "x = np.arange(0,100,0.1)   \r\n",
    "plt.plot(x,x*x)\r\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### 参考 \n",
    "- [nndl-exercise](https://github.com/nndl/exercise/blob/master/chap1_warmup/numpy_%20tutorial.ipynb)\n",
    "- [CS231N numpy tutorial](https://cs231n.github.io/python-numpy-tutorial/)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "py35-paddle1.2.0"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}